Papers
Research papers from arXiv and related sources
Improving Generalization and Trainability of Quantum Eigensolvers via Graph Neural Encoding
Determining the ground state of a many-body Hamiltonian is a central problem across physics, chemistry, and combinatorial optimization, yet it is often classically intractable due to the exponentia...
Jungyun Lee, Daniel K. Park
SHARP: A compact focusing system for medical applications using a diverging plasma lens
Cancer therapy for deep-seated tumors requires precise irradiation of a small target deep within the patient while minimizing radiation exposure to surrounding tissues. This can be accomplished wit...
Kyrre Ness Sjobak, Elisabeth Rød-Lindberg, Abélia Ellingsen, Pierre Drobniak, Vilde Flognfeldt Ri...
Model Selection in High-Dimensional Linear Regression using Boosting with Multiple Testing
High-dimensional regression specification and analysis is a complex and active area of research in statistics, machine learning, and econometrics. This paper proposes a new approach, Boosting with ...
George Kapetanios, Vasilis Sarafidis, Alexia Ventouri
Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies
We propose a framework for testing the homogeneity of conditional average treatment effects (CATEs) across multiple experimental and observational studies. Our approach leverages multiple randomize...
Ana Armendariz, Martin Huber
BayesFusion-SDF: Probabilistic Signed Distance Fusion with View Planning on CPU
Key part of robotics, augmented reality, and digital inspection is dense 3D reconstruction from depth observations. Traditional volumetric fusion techniques, including truncated signed distance fun...
Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
Exo Skryer: A JAX-accelerated sub-stellar atmospheric retrieval framework
Contemporary exoplanet and brown dwarf atmospheric research relies heavily on retrieval frameworks to recover thermal and chemical properties and perform model comparison in an observational data-d...
Elspeth K. H. Lee
A Flow Extension to Coroutine Types for Deadlock Detection in Go
Coroutines, as an abstract programming construct, are a generalization of functions that can suspend execution part- way for later resumption. Coroutine Types are behavioral types to model interact...
Qiqi Jason Gu, Lixue Liu, Wei Ke
On Instantons at Large Charge
The large R-charge limit of two-point functions of chiral primary operators in rank-one N=2 superconformal field theories exhibits a universal behavior controlled by the effective field theory on t...
Andrea Cipriani, Raffaele Savelli
Identifying and Explaining (Non-)Equivalence of First-Order Logic Formulas
First-order logic is the basis for many knowledge representation formalisms and methods. Providing technological support for learning to write first-order formulas for natural language specificatio...
Fabian Vehlken, Thomas Zeume, Emilio Carrasco Bustamante, Maëlle Cornély, Lukas Pradel
Circular V-grooves on single-crystal gold: optical properties and sensing feasibility
Single-crystal Au(111) microplates provide an ultra-smooth, low-defect platform for reproducible plasmonic nanocavities. Here we realize reflection-mode whispering-gallery metasurfaces comprising p...
Amos Sospeter Kiyumbi
Curiosity Over Hype: Modeling Motivation Language to Understand Early Outcomes in a Selective Quantum Track
We study whether latent motivation signals in short Spanish admission responses predict engagement and performance in an early quantum computing pathway run by QuantumHub Peru. We analyze N=241 app...
Daniella Alexandra Crysti Vargas Saldana, Freddy Herrera Cueva
Hardware-Accelerated Geometrical Simulation of Biological and Engineered In-Air Ultrasonic Systems
The deployment of in-air acoustic sensors for industrial monitoring and autonomous robotics has grown significantly, often drawing inspiration from biological echolocation. However, developing and ...
Wouter Jansen, Jan Steckel
Evaluating the Impact of Data Anonymization on Image Retrieval
With the growing importance of privacy regulations such as the General Data Protection Regulation, anonymizing visual data is becoming increasingly relevant across institutions. However, anonymizat...
Marvin Chen, Manuel Eberhardinger, Johannes Maucher
RAID: Retrieval-Augmented Anomaly Detection
Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on image reconstruct...
Mingxiu Cai, Zhe Zhang, Gaochang Wu, Tianyou Chai, Xiatian Zhu
Learning Mutual View Information Graph for Adaptive Adversarial Collaborative Perception
Collaborative perception (CP) enables data sharing among connected and autonomous vehicles (CAVs) to enhance driving safety. However, CP systems are vulnerable to adversarial attacks where maliciou...
Yihang Tao, Senkang Hu, Haonan An, Zhengru Fang, Hangcheng Cao, Yuguang Fang
Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks
Small and Medium Enterprises (SMEs) constitute 99.9% of U.S. businesses and generate 44% of economic activity, yet systematically identifying high-potential SMEs remains an open challenge. We intro...
Yijiashun Qi, Hanzhe Guo, Yijiazhen Qi
Metaorder modelling and identification from public data
Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-...
Ezra Goliath, Tim Gebbie
Co-Optimization of Network Topology and Variable Impedance Devices under Dynamic Line Ratings in Power Transmission Systems
Power system operators are increasingly deploying Grid Enhancing Technologies (GETs) to mitigate operational challenges such as line and transformer congestion, and voltage violations. These techno...
Junseon Park, Hyeongon Park, Rahul K. Gupta
Interpolation-Driven Machine Learning Approaches for Plume Shine Dose Estimation: A Comparison of XGBoost, Random Forest, and TabNet
Despite the success of machine learning (ML) in surrogate modeling, its use in radiation dose assessment is limited by safety-critical constraints, scarce training-ready data, and challenges in sel...
Biswajit Sadhu, Kalpak Gupte, Trijit Sadhu, S. Anand
Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization
Active data acquisition is central to many learning and optimization tasks in deep neural networks, yet remains challenging because most approaches rely on predictive uncertainty estimates that are...
Weichi Yao, Bianca Dumitrascu, Bryan R. Goldsmith, Yixin Wang